A data-driven technique for analyzing Quality-of-Transmission (QoT) data of previously established connections is proposed for accurately deciding the QoT of the newly arriving multicast requests in metro optical networks. The proposed approach is self-adaptive, it is a function of data that are independent from the physical layer impairment (PLIs) and thus does not require specific measurement equipment, and it does not assume the existence of a system with extensive processing and storage capabilities. It is also fast in processing new data, and fast in finding a near-accurate QoT model provided that such a model exists. The proposed technique can replace the existing Q-factor models that are not self-adaptive, they are a function of the PLIs, and their evaluation requires time-consuming simulations, lab experiments, specific measurement equipment, and considerable human effort. The proposed data-driven QoT approach is based on the utilization of a feed-forward neural network that is trained on a dataset previously generated from a known Q-factor model. The dataset fed to the neural network is represented in a way that specifically describes the QoT of the multicast connections requesting to be established in the network but it is independent from the PLIs. The validity of the proposed approach is examined for two distinct networks, exhibiting a high accuracy when compared to the results of the Q-factor model utilized for generating the QoT data.